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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code>.NearestNeighbors</a></li>
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  <div class="section" id="sklearn-neighbors-nearestneighbors">
<h1><a class="reference internal" href="../classes.html#module-sklearn.neighbors" title="sklearn.neighbors"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code></a>.NearestNeighbors<a class="headerlink" href="#sklearn-neighbors-nearestneighbors" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.neighbors.NearestNeighbors">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.neighbors.</code><code class="sig-name descname">NearestNeighbors</code><span class="sig-paren">(</span><em class="sig-param">n_neighbors=5</em>, <em class="sig-param">radius=1.0</em>, <em class="sig-param">algorithm='auto'</em>, <em class="sig-param">leaf_size=30</em>, <em class="sig-param">metric='minkowski'</em>, <em class="sig-param">p=2</em>, <em class="sig-param">metric_params=None</em>, <em class="sig-param">n_jobs=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_unsupervised.py#L8"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors" title="Permalink to this definition">¶</a></dt>
<dd><p>Unsupervised learner for implementing neighbor searches.</p>
<p>Read more in the <a class="reference internal" href="../neighbors.html#unsupervised-neighbors"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.9.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>n_neighbors</strong><span class="classifier">int, optional (default = 5)</span></dt><dd><p>Number of neighbors to use by default for <a class="reference internal" href="#sklearn.neighbors.NearestNeighbors.kneighbors" title="sklearn.neighbors.NearestNeighbors.kneighbors"><code class="xref py py-meth docutils literal notranslate"><span class="pre">kneighbors</span></code></a> queries.</p>
</dd>
<dt><strong>radius</strong><span class="classifier">float, optional (default = 1.0)</span></dt><dd><p>Range of parameter space to use by default for <a class="reference internal" href="#sklearn.neighbors.NearestNeighbors.radius_neighbors" title="sklearn.neighbors.NearestNeighbors.radius_neighbors"><code class="xref py py-meth docutils literal notranslate"><span class="pre">radius_neighbors</span></code></a>
queries.</p>
</dd>
<dt><strong>algorithm</strong><span class="classifier">{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional</span></dt><dd><p>Algorithm used to compute the nearest neighbors:</p>
<ul class="simple">
<li><p>‘ball_tree’ will use <a class="reference internal" href="sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree" title="sklearn.neighbors.BallTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">BallTree</span></code></a></p></li>
<li><p>‘kd_tree’ will use <a class="reference internal" href="sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree" title="sklearn.neighbors.KDTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">KDTree</span></code></a></p></li>
<li><p>‘brute’ will use a brute-force search.</p></li>
<li><p>‘auto’ will attempt to decide the most appropriate algorithm
based on the values passed to <a class="reference internal" href="#sklearn.neighbors.NearestNeighbors.fit" title="sklearn.neighbors.NearestNeighbors.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> method.</p></li>
</ul>
<p>Note: fitting on sparse input will override the setting of
this parameter, using brute force.</p>
</dd>
<dt><strong>leaf_size</strong><span class="classifier">int, optional (default = 30)</span></dt><dd><p>Leaf size passed to BallTree or KDTree.  This can affect the
speed of the construction and query, as well as the memory
required to store the tree.  The optimal value depends on the
nature of the problem.</p>
</dd>
<dt><strong>metric</strong><span class="classifier">string or callable, default ‘minkowski’</span></dt><dd><p>the distance metric to use for the tree.  The default metric is
minkowski, and with p=2 is equivalent to the standard Euclidean
metric. See the documentation of the DistanceMetric class for a
list of available metrics.
If metric is “precomputed”, X is assumed to be a distance matrix and
must be square during fit. X may be a <a class="reference internal" href="../../glossary.html#term-sparse-graph"><span class="xref std std-term">Glossary</span></a>,
in which case only “nonzero” elements may be considered neighbors.</p>
</dd>
<dt><strong>p</strong><span class="classifier">integer, optional (default = 2)</span></dt><dd><p>Parameter for the Minkowski metric from
sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
equivalent to using manhattan_distance (l1), and euclidean_distance
(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.</p>
</dd>
<dt><strong>metric_params</strong><span class="classifier">dict, optional (default = None)</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>effective_metric_</strong><span class="classifier">string</span></dt><dd><p>Metric used to compute distances to neighbors.</p>
</dd>
<dt><strong>effective_metric_params_</strong><span class="classifier">dict</span></dt><dd><p>Parameters for the metric used to compute distances to neighbors.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors.KNeighborsClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KNeighborsClassifier</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.neighbors.RadiusNeighborsClassifier.html#sklearn.neighbors.RadiusNeighborsClassifier" title="sklearn.neighbors.RadiusNeighborsClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RadiusNeighborsClassifier</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor" title="sklearn.neighbors.KNeighborsRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KNeighborsRegressor</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.neighbors.RadiusNeighborsRegressor.html#sklearn.neighbors.RadiusNeighborsRegressor" title="sklearn.neighbors.RadiusNeighborsRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RadiusNeighborsRegressor</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree" title="sklearn.neighbors.BallTree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BallTree</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>See <a class="reference internal" href="../neighbors.html#neighbors"><span class="std std-ref">Nearest Neighbors</span></a> in the online documentation
for a discussion of the choice of <code class="docutils literal notranslate"><span class="pre">algorithm</span></code> and <code class="docutils literal notranslate"><span class="pre">leaf_size</span></code>.</p>
<p><a class="reference external" href="https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm">https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm</a></p>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">samples</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
<span class="go">NearestNeighbors(...)</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">]],</span> <span class="mi">2</span><span class="p">,</span> <span class="n">return_distance</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="go">array([[2, 0]]...)</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">nbrs</span> <span class="o">=</span> <span class="n">neigh</span><span class="o">.</span><span class="n">radius_neighbors</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">]],</span> <span class="mf">0.4</span><span class="p">,</span> <span class="n">return_distance</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">nbrs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
<span class="go">array(2)</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.NearestNeighbors.fit" title="sklearn.neighbors.NearestNeighbors.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X[, y])</p></td>
<td><p>Fit the model using X as training data</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.NearestNeighbors.get_params" title="sklearn.neighbors.NearestNeighbors.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.NearestNeighbors.kneighbors" title="sklearn.neighbors.NearestNeighbors.kneighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kneighbors</span></code></a>(self[, X, n_neighbors, …])</p></td>
<td><p>Finds the K-neighbors of a point.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.NearestNeighbors.kneighbors_graph" title="sklearn.neighbors.NearestNeighbors.kneighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kneighbors_graph</span></code></a>(self[, X, n_neighbors, mode])</p></td>
<td><p>Computes the (weighted) graph of k-Neighbors for points in X</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.NearestNeighbors.radius_neighbors" title="sklearn.neighbors.NearestNeighbors.radius_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">radius_neighbors</span></code></a>(self[, X, radius, …])</p></td>
<td><p>Finds the neighbors within a given radius of a point or points.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.NearestNeighbors.radius_neighbors_graph" title="sklearn.neighbors.NearestNeighbors.radius_neighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">radius_neighbors_graph</span></code></a>(self[, X, radius, …])</p></td>
<td><p>Computes the (weighted) graph of Neighbors for points in X</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.NearestNeighbors.set_params" title="sklearn.neighbors.NearestNeighbors.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.neighbors.NearestNeighbors.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">n_neighbors=5</em>, <em class="sig-param">radius=1.0</em>, <em class="sig-param">algorithm='auto'</em>, <em class="sig-param">leaf_size=30</em>, <em class="sig-param">metric='minkowski'</em>, <em class="sig-param">p=2</em>, <em class="sig-param">metric_params=None</em>, <em class="sig-param">n_jobs=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_unsupervised.py#L108"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.NearestNeighbors.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L1159"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the model using X as training data</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix, BallTree, KDTree}</span></dt><dd><p>Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric=’precomputed’.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.NearestNeighbors.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.NearestNeighbors.kneighbors">
<code class="sig-name descname">kneighbors</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">n_neighbors=None</em>, <em class="sig-param">return_distance=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L531"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.kneighbors" title="Permalink to this definition">¶</a></dt>
<dd><p>Finds the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.</p>
</dd>
<dt><strong>n_neighbors</strong><span class="classifier">int</span></dt><dd><p>Number of neighbors to get (default is the value
passed to the constructor).</p>
</dd>
<dt><strong>return_distance</strong><span class="classifier">boolean, optional. Defaults to True.</span></dt><dd><p>If False, distances will not be returned</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>neigh_dist</strong><span class="classifier">array, shape (n_queries, n_neighbors)</span></dt><dd><p>Array representing the lengths to points, only present if
return_distance=True</p>
</dd>
<dt><strong>neigh_ind</strong><span class="classifier">array, shape (n_queries, n_neighbors)</span></dt><dd><p>Indices of the nearest points in the population matrix.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<p>In the following example, we construct a NeighborsClassifier
class from an array representing our data set and ask who’s
the closest point to [1,1,1]</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">samples</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
<span class="go">NearestNeighbors(n_neighbors=1)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]))</span>
<span class="go">(array([[0.5]]), array([[2]]))</span>
</pre></div>
</div>
<p>As you can see, it returns [[0.5]], and [[2]], which means that the
element is at distance 0.5 and is the third element of samples
(indexes start at 0). You can also query for multiple points:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">return_distance</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="go">array([[1],</span>
<span class="go">       [2]]...)</span>
</pre></div>
</div>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.NearestNeighbors.kneighbors_graph">
<code class="sig-name descname">kneighbors_graph</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">n_neighbors=None</em>, <em class="sig-param">mode='connectivity'</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L705"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.kneighbors_graph" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the (weighted) graph of k-Neighbors for points in X</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.</p>
</dd>
<dt><strong>n_neighbors</strong><span class="classifier">int</span></dt><dd><p>Number of neighbors for each sample.
(default is value passed to the constructor).</p>
</dd>
<dt><strong>mode</strong><span class="classifier">{‘connectivity’, ‘distance’}, optional</span></dt><dd><p>Type of returned matrix: ‘connectivity’ will return the
connectivity matrix with ones and zeros, in ‘distance’ the
edges are Euclidean distance between points.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>A</strong><span class="classifier">sparse graph in CSR format, shape = [n_queries, n_samples_fit]</span></dt><dd><p>n_samples_fit is the number of samples in the fitted data
A[i, j] is assigned the weight of edge that connects i to j.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="#sklearn.neighbors.NearestNeighbors.radius_neighbors_graph" title="sklearn.neighbors.NearestNeighbors.radius_neighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NearestNeighbors.radius_neighbors_graph</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">NearestNeighbors(n_neighbors=2)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">A</span> <span class="o">=</span> <span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors_graph</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">A</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
<span class="go">array([[1., 0., 1.],</span>
<span class="go">       [0., 1., 1.],</span>
<span class="go">       [1., 0., 1.]])</span>
</pre></div>
</div>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.NearestNeighbors.radius_neighbors">
<code class="sig-name descname">radius_neighbors</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">radius=None</em>, <em class="sig-param">return_distance=True</em>, <em class="sig-param">sort_results=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L828"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.radius_neighbors" title="Permalink to this definition">¶</a></dt>
<dd><p>Finds the neighbors within a given radius of a point or points.</p>
<p>Return the indices and distances of each point from the dataset
lying in a ball with size <code class="docutils literal notranslate"><span class="pre">radius</span></code> around the points of the query
array. Points lying on the boundary are included in the results.</p>
<p>The result points are <em>not</em> necessarily sorted by distance to their
query point.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">array-like, (n_samples, n_features), optional</span></dt><dd><p>The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.</p>
</dd>
<dt><strong>radius</strong><span class="classifier">float</span></dt><dd><p>Limiting distance of neighbors to return.
(default is the value passed to the constructor).</p>
</dd>
<dt><strong>return_distance</strong><span class="classifier">boolean, optional. Defaults to True.</span></dt><dd><p>If False, distances will not be returned.</p>
</dd>
<dt><strong>sort_results</strong><span class="classifier">boolean, optional. Defaults to False.</span></dt><dd><p>If True, the distances and indices will be sorted before being
returned. If False, the results will not be sorted. If
return_distance == False, setting sort_results = True will
result in an error.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.22.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>neigh_dist</strong><span class="classifier">array, shape (n_samples,) of arrays</span></dt><dd><p>Array representing the distances to each point, only present if
return_distance=True. The distance values are computed according
to the <code class="docutils literal notranslate"><span class="pre">metric</span></code> constructor parameter.</p>
</dd>
<dt><strong>neigh_ind</strong><span class="classifier">array, shape (n_samples,) of arrays</span></dt><dd><p>An array of arrays of indices of the approximate nearest points
from the population matrix that lie within a ball of size
<code class="docutils literal notranslate"><span class="pre">radius</span></code> around the query points.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>Because the number of neighbors of each point is not necessarily
equal, the results for multiple query points cannot be fit in a
standard data array.
For efficiency, <code class="docutils literal notranslate"><span class="pre">radius_neighbors</span></code> returns arrays of objects, where
each object is a 1D array of indices or distances.</p>
<p class="rubric">Examples</p>
<p>In the following example, we construct a NeighborsClassifier
class from an array representing our data set and ask who’s
the closest point to [1, 1, 1]:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">samples</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="n">radius</span><span class="o">=</span><span class="mf">1.6</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
<span class="go">NearestNeighbors(radius=1.6)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">rng</span> <span class="o">=</span> <span class="n">neigh</span><span class="o">.</span><span class="n">radius_neighbors</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">rng</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
<span class="go">[1.5 0.5]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">rng</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
<span class="go">[1 2]</span>
</pre></div>
</div>
<p>The first array returned contains the distances to all points which
are closer than 1.6, while the second array returned contains their
indices.  In general, multiple points can be queried at the same time.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.NearestNeighbors.radius_neighbors_graph">
<code class="sig-name descname">radius_neighbors_graph</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">radius=None</em>, <em class="sig-param">mode='connectivity'</em>, <em class="sig-param">sort_results=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L1007"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.radius_neighbors_graph" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the (weighted) graph of Neighbors for points in X</p>
<p>Neighborhoods are restricted the points at a distance lower than
radius.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features), default=None</span></dt><dd><p>The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.</p>
</dd>
<dt><strong>radius</strong><span class="classifier">float</span></dt><dd><p>Radius of neighborhoods.
(default is the value passed to the constructor).</p>
</dd>
<dt><strong>mode</strong><span class="classifier">{‘connectivity’, ‘distance’}, optional</span></dt><dd><p>Type of returned matrix: ‘connectivity’ will return the
connectivity matrix with ones and zeros, in ‘distance’ the
edges are Euclidean distance between points.</p>
</dd>
<dt><strong>sort_results</strong><span class="classifier">boolean, optional. Defaults to False.</span></dt><dd><p>If True, the distances and indices will be sorted before being
returned. If False, the results will not be sorted.
Only used with mode=’distance’.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.22.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>A</strong><span class="classifier">sparse graph in CSR format, shape = [n_queries, n_samples_fit]</span></dt><dd><p>n_samples_fit is the number of samples in the fitted data
A[i, j] is assigned the weight of edge that connects i to j.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.neighbors.kneighbors_graph.html#sklearn.neighbors.kneighbors_graph" title="sklearn.neighbors.kneighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kneighbors_graph</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="n">radius</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">NearestNeighbors(radius=1.5)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">A</span> <span class="o">=</span> <span class="n">neigh</span><span class="o">.</span><span class="n">radius_neighbors_graph</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">A</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
<span class="go">array([[1., 0., 1.],</span>
<span class="go">       [0., 1., 0.],</span>
<span class="go">       [1., 0., 1.]])</span>
</pre></div>
</div>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.NearestNeighbors.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

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